#!/usr/bin/python
# -*- coding: UTF-8 -*-

from PIL import Image, ImageFilter
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import time
import matplotlib.pyplot as plt
import numpy as np


def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)


# 初始化单个卷积核上的偏置值
def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)


# 输入特征 x，用卷积核W进行卷积运算，strides 为卷积核移动步长，
# padding 表示是否需要补齐边缘像素使输出图像大小不变
def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


# 对 x 进行最大池化操作，ksize进行池化的范围
def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


# 自己输入的图片进行预测
def imageprepare(path):
    im = Image.open(path)    #读取的图片所在路径，注意是28*28像素
    im.show()
    im = im.convert('L')
    tv = list(im.getdata())
    tva = np.array([x*1.0/255.0 for x in tv])
    return tva


sess = tf.InteractiveSession()

# 声明输入图片数据、类别
x = tf.placeholder('float', [None, 784])
y_ = tf.placeholder('float', [None, 10])
# 输入图片数据化
x_image = tf.reshape(x, [-1, 28, 28, 1])

W_conv1 = weight_variable([5, 5, 1, 6])
b_conv1 = bias_variable([6])
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

W_conv2 = weight_variable([5, 5, 6, 16])
b_conv2 = bias_variable([16])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

W_fc1 = weight_variable([7 * 7 * 16, 120])
# 偏置值
b_fc1 = bias_variable([120])
# 将卷积的输出展开
h_pool2_flat = tf.reshape(h_pool2, [-1, 7 * 7 * 16])
# 神经网络计算，并添加relu激活函数
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

W_fc2 = weight_variable([120, 10])
b_fc2 = bias_variable([10])
y_conv = tf.nn.softmax(tf.matmul(h_fc1, W_fc2) + b_fc2)

# 代价函数
cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv))
# 使用Adam优化算法来调整参数
train_step = tf.train.GradientDescentOptimizer(1e-4).minimize(cross_entropy)

# 测试正确率
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float32"))

# 变量初始化
sess.run(tf.initialize_all_variables())

# 获取mnist数据
mnist_data_set = input_data.read_data_sets('MNIST_data', one_hot=True)
c = []

# 进行训练
start_time = time.time()
for i in range(300):
    # 获取训练数据
    batch_xs, batch_ys = mnist_data_set.train.next_batch(200)
    # 每迭代10个batch，对当前训练数据进行测试，输出当前预测准确率
    if i % 10 == 0:
        train_accuracy = accuracy.eval(feed_dict={x: batch_xs, y_: batch_ys})
        c.append(train_accuracy)
        print("step %d , training accuracy %g" % (i, train_accuracy))
        # 计算间隔时间
        end_time = time.time()
        print('time:', (end_time - start_time))
        start_time = end_time
    # 训练数据
    train_step.run(feed_dict={x: batch_xs, y_: batch_ys})

plt.plot(c)
plt.tight_layout()
plt.savefig('cnn-tf-cifar10-2.png', dpi=200)

path = './mnist_train/train_6.bmp'
test_image = imageprepare(path)
prediction = tf.argmax(y_conv, 1)
result = prediction.eval(feed_dict={x: [test_image]}, session=sess)
print('输入的图片:' + path + ' 识别结果=' + str(result[0]))

sess.close()



